Making digital technologies work for growers Convenor: Prof. Simon Cook Curtin/Murdoch

Transcription

1 Making digital technologies work for growers Convenor: Prof. Simon Cook Curtin/Murdoch

2 27 th February 2018 Part I PRESENTATIONS MC Myrtille Lacoste Curtin University 14:30-15:50 Part II PANEL DISCUSSION MC Mike Ridout Food Agility CRC 15:50-16:30

3 - Frank D Emden Precision Agronomics Australia Precision agriculture & radiometrics - Joel Andrew MapIQ Quantifying management decisions - Dean Diepeveen DPIRD/Murdoch/Curtin Examples of digital tech applications - Simon Cook WA Premier s Fellowship Curtin & Murdoch - Mike Briers Food Agility CRC CEO - Luke Dawson CSBP OFE plans - Fiona Evans Murdoch/Curtin Predictive modelling - Roger Lawes CSIRO How do we make it all work?

4 Digital learnings & the Food Agility CRC Mike Briers OA Food Agility CRC, CEO

5 Digital agriculture & industry change Simon Cook Premier s Fellow for Food and Agriculture Curtin & Murdoch Universities

6 Where can digital tech contribute? Value Most ag tech investment is in food Most grain sold as a bulk commodity Efficiency/profitability Managing a risky environment Resilience Coping with increasing stresses Building human and natural capital Investability Analysis to secure investment Evidence to support policy, infrastructure

7 Is the potential gain attractive enough? Yield gap Western Region 1.3 t/ha [National Paddock Survey, Roger Lawes, CSIRO] Massive un-managed variation WA growers spend +/- $350k/y on fertilizer: What s it doing? Spread of farm returns Planfarm Bankwest: Top quartile 10.7%; Bottom quartile = 3.0% G x E management. A major opportunity Anderson [2010] 80% of variation due to environment Thanks to Trevor Syme and Matt McNee

8 How to get value from tech No-one knows where the value is coming from The toughest challenge Build business models around tech GGA Essential to combine tech, agronomy and management Change management requires engagement and co-innovation Wilko

9 Focus on On-Farm Experimentation Build on farmers experimental curiosity Use existing technology Develop models of co-learning and innovation Generate value through better decisions

10 Targeted ripping using gamma radiometrics Frank D Emden

11 Data collection, processing and ground-truthing

12 Zone mapping and transfer

13 Benefits Client 25% fuel saving 8kph vs 5kph 550mm vs mm ~50% of area Tech provider Working assets (survey equipment) Systems integration Agronomic insight Enhanced reputation

14 Making digital technologies work for growers

15 Mapping management: experiences with OFE Joel Andrew MapIQ

16 Quantifying the impact of management decisions using on-farm experiments Joel Andrew GRDC Crop Updates 27 February 2018

17 Are we achieving what we want with our management decisions? On-Farm Experimentation (OFE) is a tool to answer this question Two main reasons for OFE 1. Calibrating variable rate application - Assess if our zones and application rates were appropriate 2. Trial large capital or high risk practices - Deep & very deep ripping, on-farm lime

18 Calibrating variable rate application Current hardware and software allow variable rate application of inputs Analysis of data guides where the inputs are to go Yield Soil Tests Soil mapping + + VRA Maps =

19 Calibrating variable rate application Quantify the yield and economic benefit of carrying out the practice For each production zone: Was the low rate low enough? Was the high rate high enough? Was it better than what we did before?

20 Trial large capital or high risk practices A low risk approach to assessing the production response to a practice Learn on a small scale before implementing on a large scale

21 Trial large capital or high risk practices A low risk approach to assessing the production response to a practice Learn on a small scale before implementing on a large scale

22 Examples of digital tech applications to OFE Dean Diepeveen DPIRD Murdoch Curtin

23 Example of digital technologies in agriculture potential applications to OFE Dr Dean Diepeveen Research Officer (DPIRD) + Adjunct Professor (Murdoch Uni) + Visiting Associate Professor (Curtin Uni) Karl Svatos, PhD student, Murdoch Uni Acknowledgements Scientific AeroSpace Digital Agriculture research group at Curtin/Murdoch uni Rowan Maddern (GRDC)

24 Background I will be presenting another dataset coming your way NDVI multispectral + thermal camera on a UAV Barley crop at late-flowering to early-yellowing-off DPIRD Katanning Research Station BigData is moving into agriculture in a big way Data-analytic companies/startup More sensors/data-capture on machinery (GPS) RFID-based traceability systems Transparency in marketing (eg. blockchain) Data analytics for faster supply - to prevent food spoilage

25 UAV flying over trial Katanning

26 Thermal image of trial (5.30am) Thermal image of trial site (5.30am) Overall temperature of crop surface was 8-9 degrees. Notice patches of yellow, these are greener plotareas ( ~ degrees) The greenness is likely related to later maturing varieties Interesting notice that under the trees are warmer?

27 Thermal image of trial (2.30pm) Thermal image of trial site (1.30pm) Pathways hotter than the crop (> 5 degrees) Ryegrass crop cooler than barley crop (~1-2 degrees) greener See area between trials crop quite green (~ 8-10 degrees cooler)

28 Thermal images (higher resolution)

29 Thermal images of side of canopy Oats buffer Thermal image of canopy (early afternoon 1.30pm) Temperature gradient across canopy hotter inside (~6-9 degree hotter in canopy) The grain in the heads are hotter than the stem/leaves (~2-4 degrees) Thermal image of canopy (early morning 5.30am) Temperature gradient across canopy hotter inside (~6-9 degree hotter in canopy)

30 morning NDVI multispectral image (5.30am)

31 afternoon NDVI multispectral image (2.30pm)

32 Summary Images taken at late-flowering (starting to yellow off ) We expected to see differences in maturity but found predominately canopy differences We would expect this may be useful tool to assess frost and/or crop damage (ie. different heating/cooling properties of the canopy) May be able to use canopy density as a means for calculating yield?

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34 OFE plans for 2018 and beyond Luke Dawson CSBP

35 CRC Food Agility- CSBP Luke Dawson- CSBP Senior Agronomist

36 Why CSBP has a research/adoption focus Plot trials prove the concept- farm scale trials helps implement the concept Removes the limitations of plot trials Farmer scale machinery- stats hard to achieve previously Incorporates all paddock variables Higher adoption rate with farmer experience

37 What Amelioration Increased efficiencies from application techniques Tillage Nutrient rates Product development Keep it simple- 1 or 2 variables

38 How Fertview NUlogic- soil and plant testing In season imagery

39 Predictive modelling using OFE Fiona Evans Murdoch & Curtin Universities

40 Dr Fiona Evans, Premiers EMC Fellow, Big Data in Agriculture Predictive modelling using on farm experiments

41 2016 N 2016 Yield 2015 Yield Generalised additive models to separate N effects from spatial variation -1se s(longitude,latitude,28.19) +1se ANOVA (, ) Latitude ** ** Yield (tonnes/ha) Yield response to N Flat response curve using 1.5t/ha yield potential and typical starting soil N and N efficiencies! Rate

42 Yield potential varies spatially as well as temporally! We can estimate SPATIALLY VARYING potential yield using paddock yield maps

43 Can we estimate more accurate SPATIALLY VARYING response curves with more data? 2016 N 2016 Yield 2015 Yield More data bigger experiments! More spread of data wider range of applications Does it really matter experiment on change that don t have flat response curves

44 Challenging the status quo: how do we make it all work? Roger Lawes CSIRO

45 Motivation Enabling technology Decision and practice change

46 Grains Research and Development Corporation (GRDC) A Level 4, East Building, 4 National Circuit, Barton, ACT 2600 Australia P PO Box 5367 Kingston, ACT 2604 Australia T F #GRDCUpdates